Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	105,163
2	South Dakota	92,920
3	Iowa	74,850
4	Wisconsin	72,294
5	Nebraska	68,511
6	Utah	63,076
7	Montana	60,254
8	Wyoming	59,622
9	Illinois	59,202
10	Idaho	58,895
11	Minnesota	58,077
12	Kansas	56,630
13	Rhode Island	55,698
14	Tennessee	55,023
15	Arkansas	53,522
16	Mississippi	52,708
17	Indiana	52,541
18	Alabama	52,379
19	Missouri	52,369
20	Louisiana	51,913
21	Nevada	51,161
22	Oklahoma	51,135
23	New Mexico	48,150
24	Florida	47,404
25	Arizona	47,113
26	Alaska	45,949
27	Texas	44,599
28	Georgia	43,741
29	South Carolina	42,891
30	Kentucky	42,766
31	Colorado	42,253
32	Michigan	40,278
33	New Jersey	39,184
34	Delaware	37,686
35	Ohio	37,464
36	North Carolina	35,518
37	New York	34,390
38	Connecticut	34,057
39	Massachusetts	33,983
40	Maryland	33,688
41	California	32,190
42	District of Columbia	30,948
43	Pennsylvania	29,787
44	Virginia	28,408
45	West Virginia	27,846
46	Puerto Rico	27,342
47	Washington	23,365
48	Oregon	18,542
49	New Hampshire	16,424
50	Hawaii	12,795
51	Maine	9,081
52	Vermont	7,149

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,589
2	Wyoming	1,162
3	Kansas	1,098
4	Nebraska	1,044
5	New Mexico	881
6	Indiana	876
7	Utah	874
8	South Dakota	867
9	Minnesota	857
10	Idaho	837
11	Tennessee	827
12	Connecticut	826
13	Michigan	823
14	Kentucky	813
15	Montana	810
16	Iowa	789
17	Alaska	782
18	Arizona	775
19	Colorado	767
20	Illinois	755
21	Nevada	731
22	Wisconsin	679
23	Ohio	670
24	Alabama	652
25	Louisiana	651
26	Missouri	651
27	North Dakota	646
28	Arkansas	582
29	Oklahoma	572
30	Mississippi	569
31	West Virginia	540
32	Texas	530
33	Pennsylvania	519
34	Delaware	495
35	New Hampshire	454
36	New Jersey	454
37	Massachusetts	448
38	California	442
39	Washington	420
40	Florida	395
41	New York	395
42	Maryland	382
43	Georgia	379
44	North Carolina	330
45	Oregon	322
46	South Carolina	304
47	Virginia	255
48	Puerto Rico	209
49	Vermont	192
50	District of Columbia	186
51	Maine	173
52	Hawaii	45

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,930
2	New York	1,762
3	Massachusetts	1,570
4	Connecticut	1,427
5	Louisiana	1,398
6	Rhode Island	1,313
7	Mississippi	1,293
8	North Dakota	1,275
9	South Dakota	1,124
10	Illinois	1,060
11	Michigan	985
12	District of Columbia	977
13	Arizona	928
14	Indiana	887
15	Georgia	876
16	Florida	874
17	South Carolina	863
18	Pennsylvania	843
19	Arkansas	835
20	Delaware	799
21	Iowa	798
22	Maryland	779
23	New Mexico	776
24	Texas	773
25	Alabama	756
26	Nevada	722
27	Missouri	681
28	Tennessee	678
29	Montana	667
30	Minnesota	665
31	Wisconsin	638
32	Nebraska	585
33	Kansas	576
34	Ohio	570
35	Colorado	560
36	Idaho	560
37	North Carolina	515
38	California	491
39	Virginia	481
40	Kentucky	475
41	Oklahoma	457
42	West Virginia	434
43	Wyoming	397
44	New Hampshire	394
45	Washington	391
46	Puerto Rico	358
47	Utah	282
48	Oregon	228
49	Hawaii	170
50	Maine	162
51	Alaska	158
52	Vermont	118

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	20
2	South Dakota	19
3	Nebraska	18
4	Kansas	17
5	Idaho	14
6	Illinois	14
7	Indiana	14
8	New Mexico	14
9	Rhode Island	14
10	Connecticut	12
11	Iowa	12
12	Michigan	12
13	Missouri	12
14	Montana	12
15	Wisconsin	12
16	Colorado	11
17	Pennsylvania	10
18	Alabama	9
19	Nevada	9
20	West Virginia	9
21	Wyoming	8
22	Ohio	7
23	Kentucky	6
24	Louisiana	6
25	Maine	6
26	Minnesota	6
27	New Jersey	6
28	Oklahoma	6
29	Tennessee	6
30	Texas	6
31	Washington	6
32	Arizona	5
33	Arkansas	5
34	Mississippi	5
35	Puerto Rico	5
36	South Carolina	5
37	District of Columbia	4
38	Florida	4
39	Maryland	4
40	Massachusetts	4
41	North Carolina	4
42	Oregon	4
43	Delaware	3
44	Georgia	3
45	New York	3
46	Utah	3
47	Vermont	3
48	California	2
49	New Hampshire	2
50	Virginia	2
51	Alaska	0
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Norton	Kansas	206,491	1	99
Crowley	Colorado	204,917	2	99
Bon Homme	South Dakota	198,812	3	99
Buffalo	South Dakota	195,209	4	99
Lincoln	Arkansas	193,412	5	99
Davidson	Tennessee	68,692	505	83
Richland	South Carolina	49,748	1253	60
York	South Carolina	35,803	2058	34
Orange	California	26,086	2536	19
Pierce	Washington	20,786	2737	12

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	6,458	2	99
Dickey	North Dakota	5,952	3	99
Hancock	Georgia	5,439	4	99
Emporia city	Virginia	5,238	5	99
Richland	South Carolina	729	1336	57
Davidson	Tennessee	612	1586	49
Orange	California	497	1860	40
York	South Carolina	491	1877	40
Pierce	Washington	361	2220	29

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.